Marc F. Bellemare Posts

My paper with Dan Millimet titled “On the (Mis) Use of the Fixed Effects Estimator” has been accepted and is now forthcoming at the Oxford Bulletin of Economics and Statistics. If you want a link to a .pdf of the accepted version of the paper or to a Stata .do file showing you how to use the alternative estimators we discuss in the paper, scroll all the way to the end of this post. If you want a bit of storytelling about how this paper came about, and what it does, read on.

My paper with Bernhard Dalheimer titled “Global Agricultural Value Chains and Food Prices” has been accepted and is now forthcoming at the American Journal of Agricultural Economics.

I am glad that this is finally accepted for publication: Bernhard and I first discussed it in 2022 when he was here for his postdoc, and he began presenting it when he was on the job market that same year, in early 2023. Since then, the paper has only gotten better as a result of comments from colleagues. I really like that it marries Bernhard’s interests in international trade and agricultural value chains with my own interest in food prices and agricultural value chains.

The job market for econ PhDs is bad. It’s not just bad: It’s as-bad-if-not-worse-than-during-a-global-pandemic bad. Here is a screen capture of the data visualization the American Economics Association has on its Job Openings for Economists website:

The story here is not just that the job market is bad this year; the story is that, with the exception of an uptick in 2022, the job market has been going from bad to worse over the last six years. I knew this anecdotally from looking at how my PhD students on the job market have been doing over that time period, but it is sobering to see my intuition supported by actual job market data.

I have heard a variety of reasons for why this is happening. “The Trump administration doesn’t like experts!” Yeah? Doesn’t explain why the trend predates January 2025 by quite a few years. “AI is replacing us!” Be that as it may, ChatGPT, Claude, and Gemini weren’t exactly household names in 2023 or even early 2024. “There’s an enrollment cliff coming!” Sure, è pericoloso sporgersi, but since when do teaching needs directly dictate hiring needs, under the “if you build it, they will come” logic? And so on, and so forth.

For all I know, it’s a question of… economics. See, for years there was this profession where you were all but guaranteed a really good job—one that would reward you handsomely with money, the freedom to work on whatever you felt like or both, in some rare and much-coveted jobs at top departments—if only you were willing to put in the hard work of getting a PhD. And for a long time, this was true year in, year out.

I know that economic theory is viewed as old-fashioned by and has fallen out of favor with younger people, but anyone who has done a PhD in economics (or even a PhD in something econ-adjacent like business or public policy) has heard of allocative efficiency, the idea whereby resources tend to flow to where their productivity (and thus their wage or rent) is the highest. Given that, is it any surprise that bright young people flocked to econ (and econ-adjacent) PhD programs? Is it any surprise that eventually the gap between demand and supply would be filled, and that there would then be an excess supply of workers in that lucrative profession, especially given the five-, six-, and even seven-year time lag between the start of a PhD and the job market’s verdict on a fresh PhD?

But there’s also another, no-less-important fact that seems to be getting lost in discussions of how bad the job market is. I alluded somewhat snarkily above to the fact that economic theory is viewed as old-fashioned by and has fallen out of favor with younger people. At best, it is seen as yet another entry barrier to be dispensed with via coursework and sundry qualifying exams. I cannot speak for macro or for more structural fields (e.g., industrial organization), but as someone who is working under the broad umbrella of reduced-form applied microeconomics, I have seen the field change from relying heavily on theory to derive testable predictions that were tested almost exclusively with observational data in the early 2000s to something much more atheoretical relying on causal inference methods, with or without experimental data.

One of my coauthors likes to joke that difference-in-differences is all younger people seem to know about nowadays. That is a bit unfair, but it does contain a kernel of truth: A lot of the work done in applied micro nowadays is almost entirely empirical, often with little to no discussion of the economics of the application—and that’s if and when people look at an application that is actually economic in nature rather than looking at questions that seem more relevant to other disciplines.

As I have been telling graduate students lately: The empirical methods you use—diff-in-diffs, for sure, but also RCTs, IV, RDD, synthetic control, shift-share, etc.—are tools that are accessible to people in other disciplines just as much as they are available to you. In fact, there are social scientists who do excellent work writing about econometrics from outside of economics (a lot of them were trained as political scientists and teach in political science departments; people like Matt Blackwell, Adam Glynn, Cyrus Samii, Maya Sen, and so on), and they often do a much better job of explaining their contributions to applied researchers than your friendly neighborhood Real Rigorous Econometrician™. So at the end of the day, if you are going to be doing work that is hard to distinguish from the work done by applied researchers in other disciplines, you are only setting yourself up as a more expensive version of what those other disciplines can offer.¹

So what is to be done, as Lenin famously asked? For starters, one might want to bring economic theory back into what they are doing. The one thing that separates economists from other social scientists is that economic theory gives us a way to analyze the world—one that often leads to conclusions that are counterintuitive or surprising. I’m not arguing for full-blown structural work, just for bringing back economics in (reduced-form) applied micro research.²

1. “But we know about identification and do a better job of identifying causal relationships than other disciplines!” Bless your heart. During those Seven Blighted Years during which I taught at a policy school, first-semester MPP students were taught about causal inference. ↩︎
2. And again, I am not a macroeconomist nor am I a structural econometrician, and so I do not speak for what those guys do. ↩︎